Park Yang Shin, Seo Joon Beom, Kim Namkug, Chae Eun Jin, Oh Yeon Mok, Lee Sang Do, Lee Youngjoo, Kang Suk-Ho
Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea.
Invest Radiol. 2008 Jun;43(6):395-402. doi: 10.1097/RLI.0b013e31816901c7.
To develop a system for texture-based quantification of emphysema on high-resolution computed tomography (HRCT) and to compare it with density-based quantification in correlation with pulmonary function test (PFT).
Two hundred sixty-one circular regions of interest (ROI) with 16-pixel diameter [66 ROIs representing typical area of normal lung; 69 representing bronchiolitis obliterans (BO); 64, mild emphysema (ME); and 62, severe emphysema (SE)] were used to train the automated classification system based on the Support Vector Machine classifier and on variable texture and shape features. An automated quantification system was developed with a moving ROI in the lung area, which classified each pixel into 4 categories. To validate the system, the HRCT and standard-kernel-reconstructed volumetric CT data of 39 consecutive patients with emphysema were included. Using this system, the whole lung area was evaluated, and the area fractions of each class were calculated (normal lung%, BO%, ME%, SE%, respectively). The emphysema index (EI) of texture-based quantification was defined as follows: (0.3 x ME% + SE%) (TEI). EIs from density-based quantification with a threshold of -950 Hounsfield Units, were measured on both HRCT (DEI_HR_2D) and on volumetric CT (DEI_standard_3D). The agreement between TEI, DEI_HR_2D, and DEI_standard_3D was assessed using interclass correlation coefficients (ICC). Correlation of the results on the TEI with the PFT results was compared with the results of the DEI_standard_3D and the DEI_HR_2D with Spearman's correlation test. To evaluate the contribution of each texture-based quantification lesion (BO%, ME%, SE%) on PFT, multiple linear regression analysis was performed.
The calculated TEI (19.71% +/- 17.98%) was well correlated with the DEI_standard_3D (19.42% +/- 14.30%) (ICC = 0.95), whereas the ICC with DEI_HR_2D (37.22% +/- 9.42%) was 0.43. TEI showed better correlation with PFT than DEI_standard_3D or DEI_HR_2D did [R = 0.71 vs. 0.67 vs. 0.61 for forced expiratory volume in 1 second (FEV(1))/forced vital capacity (FVC); 0.54 vs. 0.50 vs. 0.43 for diffusing capacity (DLco), respectively]. Multiple linear regression analysis revealed that the BO% and SE% areas were independent determinants of FEV(1)/FVC, whereas the ME% and the SE% were determinants of DLco.
Texture-based quantification of emphysema using an automated system showed better correlation with the PFT results than density-based quantification. Separate quantification of the BO, ME, and SE areas showed a different contribution of each component to the FEV(1)/FVC and the DLco. The proposed system can be successfully used for detailed regional and global evaluation of lung lesions on HRCT scanning for emphysema.
开发一种基于纹理的高分辨率计算机断层扫描（HRCT）肺气肿定量系统，并将其与基于密度的定量方法进行比较，以研究其与肺功能测试（PFT）的相关性。
使用261个直径为16像素的圆形感兴趣区域（ROI）[66个ROI代表正常肺的典型区域；69个代表闭塞性细支气管炎（BO）；64个代表轻度肺气肿（ME）；62个代表重度肺气肿（SE）]，基于支持向量机分类器以及可变纹理和形状特征训练自动分类系统。开发了一种在肺区域使用移动ROI的自动定量系统，该系统将每个像素分为4类。为验证该系统，纳入了39例连续性肺气肿患者的HRCT和标准内核重建的容积CT数据。使用该系统评估全肺区域，并计算每个类别的面积分数（分别为正常肺%、BO%、ME%、SE%）。基于纹理定量的肺气肿指数（EI）定义如下：（0.3×ME% + SE%）（TEI）。在HRCT（DEI_HR_2D）和容积CT（DEI_standard_3D）上测量基于密度定量且阈值为-950亨氏单位的EI。使用组内相关系数（ICC）评估TEI、DEI_HR_2D和DEI_standard_3D之间的一致性。通过Spearman相关检验比较TEI结果与PFT结果的相关性以及DEI_standard_3D和DEI_HR_2D的结果。为评估基于纹理定量的每个病变区域（BO%、ME%、SE%）对PFT的贡献，进行多元线性回归分析。
计算得到的TEI（19.71%±17.98%）与DEI_standard_3D（19.42%±14.30%）具有良好的相关性（ICC = 0.95），而与DEI_HR_2D（37.22%±9.42%）的ICC为0.43。TEI与PFT的相关性优于DEI_standard_3D或DEI_HR_2D [1秒用力呼气容积（FEV(1)）/用力肺活量（FVC）的R值分别为0.71对0.67对0.61；弥散量（DLco）的R值分别为0.54对0.50对0.43]。多元线性回归分析显示，BO%和SE%区域是FEV(1)/FVC的独立决定因素，而ME%和SE%是DLco的决定因素。
使用自动系统基于纹理的肺气肿定量与PFT结果的相关性优于基于密度的定量。对BO、ME和SE区域进行单独定量显示每个组分对FEV(1)/FVC和DLco的贡献不同。所提出的系统可成功用于HRCT扫描对肺气肿肺病变进行详细的区域和整体评估。
